AI for Science · Industrial CAE Software · Composite Mechanics
- M.Eng. Aerospace Engineering, Xi'an Jiaotong University — GPA 3.89/4.0
- B.Eng. Engineering Mechanics, Wuhan University of Science and Technology — GPA 3.77/4.0
- Intern Beijing Shenzhou Aerospace Software Technology Co., Ltd. — Industrial CAE R&D
Bridging computational solid mechanics and machine learning to build next-generation intelligent simulation tools.
- AI for Science (AI4S): Physics-informed & data-driven models for materials and structures
- Composite Materials Mechanics: Failure criteria, constitutive modeling, damage evolution, multi-scale analysis
- Intelligent CAE: ML-accelerated solvers, surrogate modeling, reduced-order models (ROM)
- Industrial Software R&D: CAD/CAE kernel development, solver architecture, FreeCAD secondary development
- ABAQUS Secondary Development: Parametric modeling (Python scripting / GUI plugins), UMAT/VUMAT/USDFLD subroutine authoring, cohesive element and PBC applying
- Structural analysis: buckling, progressive damage, thermo-mechanical coupling
- Architectures: DNN · CNN (ResNet) · ICNN (Input Convex NN) · GAN · DeepONet · PINNs
- Applications: Failure envelope prediction, surrogate constitutive modeling, operator learning for PDEs, deep transfer learning (DTL)
- Frameworks: PyTorch · TensorFlow · Scikit-learn
- LLM/Agent: Ollama · ChromaDB · LangChain · Claude Code (agentic workflows)
- Python — ML pipelines, ABAQUS scripting, GUI (PyQt6 / PyVista), scientific computing
- C++ / Fortran — Solver kernels, constitutive model implementation (Eigen, pybind11)
- Parallel Computing: OpenMP · MPI · CUDA (cuSPARSE)
- Dev environment: VS2022 · WSL2 (Ubuntu) · Intel oneAPI · Anaconda
| Project | Description | Stack |
|---|---|---|
| CSDM | Multi-agent AI system for composite stiffened panel structural design; 6-agent pipeline from NL input → LLM candidate gen + RAG retrieval + surrogate screening → ABAQUS FEM validation → report | Python · LLM · RAG · ABAQUS |
| MDD-TFC | Model-data-driven transverse failure criterion for UD-CFRP; ResNet-101 + Tsai-Wu + ICNN maps RVE microstructure to full failure envelope via only 4 load cases; validated via few-shot transfer learning (TWS Q1, 1st author) | PyTorch · ABAQUS |
| FE-NN-KT | Multi-scale homogenization framework: offline RVE sampling → surrogate constitutive model training → online FE-NN macro solver; coarse-to-fine transfer reduces data cost 3.3×, macro analysis 20× faster than DNS | PyTorch · ABAQUS · Q4 FEM |
| Composite Intelligent Design Platform | Hierarchical design platform (microstructure → laminate → stiffened panel → fuselage); sub-second inference with per-level AI models; supports user fine-tuning | PyQt6 · TF · PyTorch |
| CAE FEM Software | Lightweight FEM solver: PyQt6 + PyVista frontend, C++/Eigen compute core, pybind11 bridge, Gmsh meshing, real-time stress/displacement rendering | C++ · PyQt6 · pybind11 · Gmsh |
Papers (2 SCI papers, total IF 15.5):
- 📝 Liu Z, et al. A model and data-driven transverse failure criterion for UD-CFRP composites. Thin-Walled Structures, 2026. (Q1 Top, 1st author)
- 📝 Wang Q, Li Z, Liu Z, et al. Interpretable prediction of ultimate strength of composite pressure hulls using symbolic regression. Composites Part A, 2026. (Q2 Top, 3rd author)
Software Copyrights (4×, all 1st inventor):
- 💻 航空复合材料多层级智能设计平台 V1.0
- 💻 复合材料参数化建模ABAQUS插件 V1.0
- 💻 通用CAE求解系统 V1.0
- 💻 Neper-PFC数据转化接口 V1.0
Awards:
- 🏆 Competition: MCM/ICM M Prize · 华为杯研究生数学建模二等奖 · 全国大学生数学竞赛二等奖 · 20+ total
- 🎓 Scholarships: National Scholarship (2023)
- Custom ABAQUS plugins & batch simulation pipelines (parametric modeling, pre/post-processing)
- UMAT/VUMAT subroutine development for composite constitutive models
- ML surrogate models for mechanical property prediction & inverse design
- Multi-agent AI systems for simulation automation (LLM + RAG + FEM in the loop)
- DeepONet / PINN implementation for engineering PDE problems
- CAE solver prototyping with C++/Python/Fortran hybrid architecture
- 📧 lzp03@stu.xjtu.edu.cn
- 💬 WeChat:
lzp13971812916 - 🐙 GitHub: @ZPL-03